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Hierarchical latent tree analysis is an alternative to LDA, which models word co-occurrence using a tree of latent variables and the states of the latent variables, which correspond to soft clusters of documents, are interpreted as topics. Animation of the topic detection process in a document-word matrix through biclustering. Every column ...
Thematic analysis goes beyond simply counting phrases or words in a text (as in content analysis) and explores explicit and implicit meanings within the data. [2] Coding is the primary process for developing themes by identifying items of analytic interest in the data and tagging these with a coding label. [ 4 ]
A co-occurrence network created with KH Coder. Co-occurrence network, sometimes referred to as a semantic network, [1] is a method to analyze text that includes a graphic visualization of potential relationships between people, organizations, concepts, biological organisms like bacteria [2] or other entities represented within written material.
Content analysis is the study of documents and communication artifacts, which might be texts of various formats, pictures, audio or video. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. [1]
The semantic content of a document is composed by combining one or more terms from one or more topics. Certain terms are ambiguous , belonging to more than one topic, with different probability. (For example, the term training can apply to both dogs and cats, but are more likely to refer to dogs, which are used as work animals or participate in ...
The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents.
Topic modeling to extract the main themes using NNMF and Factor Analysis. Correspondence analysis in order to identify words or concepts (or content categories) associated with any categorical meta-data associated with documents. Pre-and post-processing with R and python script; Analyze more than 70 languages including Chinese, Japanese, Korean ...
Cognitive discourse analysis (CODA) is a research method which examines natural language data in order to gain insights into patterns in (verbalisable) thought. [ 1 ] [ 2 ] The term was coined by Thora Tenbrink [ 3 ] to describe a kind of discourse analysis that had been carried out by researchers in linguistics and other fields.